In principle, it is hard to disagree with Pope Leo XIV, who argues in Magnifica Humanitas that humans values and artificial intelligence must be balanced.
Some critics will complain about AI ownership concentration and outsized market power, to protect human values.
But markets generally develop with a few leaders, whether we like it or not.
So we still are left with the thorny task of figuring out how to do all that balancing.
Consider similar concerns about the internet. In the late 1980s and early 1990s, many academics, researchers, and early users believed the internet should remain a non-commercial, collaborative environment.
After all, the early internet was a subsidized, academic network encouraging sharing and open exchange.
The early internet (ARPANET, NSFNET, and connected university networks) was funded almost entirely by governments and research institutions.
This culture produced enduring norms:
The turning point came after restrictions on commercial traffic over the NSFNET were lifted in the early 1990s:
private Internet Service Providers appeared
domain registration expanded
browsers made the web accessible
online retail became feasible
venture capital entered the industry.
So some worried about:
commercialization overwhelming academic culture
advertising degrading user experience
unequal access
concentration of economic power.
Concerns about concentration of power will resemble earlier concerns about the internet.
But the emphasis on “free” might not happen.
State-of-the-art AI models have substantial ongoing inference costs, so the marginal cost of serving each additional user is not close to zero. And near-zero marginal costs were the enabler for “free” internet services and apps.
As a result, "everything should be free" is less economically sustainable for AI than it was for web content.
On the other hand, concerns about concentration of power have already emerged. But it’s a balance. Without the prospect of profit, much less capital would have flowed into software and internet infrastructure, economists will argue.
And it is likely the rule of three will emerge in various segments of the overall AI market, as is true for capital-intensive markets.
source: Mercatus
The rule of three is the idea that in many competitive industries, market structure tends to settle into a small number of dominant firms because scale, fixed costs, and network effects push markets toward concentration rather than endless fragmentation.
That often leads to a winner takes all market structure.
In AI, that logic can show up at multiple layers: a few chipmakers can dominate hardware, a few foundation-model providers can dominate models, a few cloud/enterprise ecosystems can dominate platforms, and a few application software vendors can dominate key use cases:
Hardware. AI chips and the infrastructure around them are capital-intensive, with high fixed costs and strong scale advantages, which makes concentration likely.
Models. Frontier model development also has steep training costs, data advantages, and distribution effects, so a small set of model leaders can emerge even if many models exist in the long tail.
Platforms. Cloud and AI distribution layers can become winner-take-most because users gravitate to ecosystems with the best tooling, trust, integrations, and developer gravity.
Software. Application layers are often more fragmented than infrastructure, but in categories with strong workflow lock-in or standards, the same top-three pattern can appear.
Not all Industries feature the rule of three pattern. That can occur when:
Examples include many local services, artisanal goods, custom professional services, and some labor-intensive niches where geography and relationships matter more than national scale. Sectors with rapid product churn and low switching costs can also resist stable three-firm dominance because new entrants can displace incumbents quickly.
It’s hard to see how the various parts of the AI market can avoid developing along a rule of three pattern.
And that means some critics will be severely disappointed.
